Model-based inversion of ultrasonic guided waves for cortical bone properties
Bibliographic record
Abstract
Axial transmission ultrasonography, which uses a set of transmitting and receiving probes placed on the same waveguide’s surface, shows the potential clinical application for cortical bone quality assessment. In this work, a model-based parameter sweep inversion approach has been developed to estimate the thickness and elastic velocities of the cortex from the dispersive axially-transmitted snapshots. The inversion algorithm is formulated in the frequency-phase velocity (f-c) domain. To solve the inverse problem, i.e., to extract bone properties from ultrasound data, a forward modeling has been developed to simulate the f-c dispersion curves given a bone model. A semi-analytical finite element (SAFE) method is used to compute the dispersion curves for a complex structure of a cortical bone plate coupled with overlying soft tissues. A parameter sweep is used to seek within a range of values an optimized solution with the least misfit. The proposed method optimizes the mismatch between the measured and theoretically calculated dispersion curves with a least-square constraint. Numerical and in-vivo experimental data examples are presented to illustrate the technique’s performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".